Deep Learning Model for Financial Time Series Prediction

Abstract

Stock market is considered complex, fickle, and dynamic. Undoubtedly, prediction of its price is one of the most challenging tasks in time series forecasting. Traditionally, there are several techniques to effectively predict the next t lag of time series data such as Logistic Regression and Random Forest. With the recent progression in sophisticated machine learning approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. This paper employs Long-Short Term Memory (LSTM) deep learning approach to predict future prices for low, medium, and high risk stocks. To the best of our knowledge, we are proposing an innovating technique to evaluate deep learning and other prediction techniques w.r.t. the stocks’ risk factor. The proposed approach is compared with other traditional algorithms over different periods of training data. The results show that our LSTM approach outperforms other traditional approaches for all stock categories over different time periods. Experimental results illustrate that, for low and medium risk stocks, it is better to use LSTM with long time period of training data. However, for high risk stocks, short time period of training data provides more accurate predictions.